The latest AI news, launches, models, companies and research — collected from official sources, research feeds and reporting, grouped into stories and ranked by the BharatHunt Trend Score.
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5345 stories
Same Text, Different Numbers: The Divergence of LLM-Based Measures. The story centres on Inference. Reported by arXiv. Bharat Hunt files it under AI Models and AI Research — the section covering a new or updated model, its capabilities, benchmarks or availability.
G$^2$PTQ: Improving LLM Post-Training Quantization with Generalized Gradient Compensation. It centres on AI Safety, and also names GitHub. Reported by arXiv. Bharat Hunt files it under AI Models and AI Research — the section covering a new or updated model, its capabilities, benchmarks or availability.
ZooWork-ShopRanker: An Open, Preference-Aligned E-Commerce Reranker. The story centres on Benchmarks. Reported by arXiv. Bharat Hunt files it under AI Models — the section covering a new or updated model, its capabilities, benchmarks or availability. Open the original report for the full detail.
THA: Weighted Finite-State Text Normalization and Inverse Text Normalization for Khmer. The story centres on Google. Reported by arXiv. Bharat Hunt files it under Open Source AI and AI Tools — the section covering open-weight models, permissive licences and community releases.
Does Uniform Discrete Diffusion Need Time? Reported by arXiv. Bharat Hunt files it under Generative AI and AI Models — the section covering text, image, audio and video generation — products and their output. Open the original report for the full detail.
FAVoR: Measuring and Mitigating Author-Style Homogenization in Federated Personalized Generation. It centres on Fine-tuning, and also names Benchmarks. Reported by arXiv. Bharat Hunt files it under AI Models — the section covering a new or updated model, its capabilities, benchmarks or availability.
MWE-ECL: Recoverable Long-Range Context Does Not Always Override Local Lexical Priors. The story centres on DeepSeek. Reported by arXiv. Bharat Hunt files it under AI Models and Enterprise AI — the section covering a new or updated model, its capabilities, benchmarks or availability.
A Taxonomy-Driven Synthetic Dataset for Retrieval Effectiveness and Fairness Evaluation in Hiring. The story centres on Benchmarks. Reported by arXiv. Bharat Hunt files it under AI Research and AI Startups — the section covering papers, benchmarks, evaluations, interpretability and safety results.
BMMS: Policy-Conditioned Measurement of Behavioral Compliance in Language Models. It centres on Benchmarks, and also names GPT and OpenAI. Reported by arXiv. Bharat Hunt files it under AI Regulation and AI Models — the section covering law, policy, courts, standards and government positions on AI.
Loop Before You Leap: What Recurrent Depth Can and Cannot Buy in Data-Limited Pretraining. It centres on Multimodal, and also names Inference. Reported by arXiv. Bharat Hunt files it under AI Models and AI Funding — the section covering a new or updated model, its capabilities, benchmarks or availability.
Editing Large Language Models with Geometry-Aware Regularization. It centres on GPT, and also names Llama. Reported by arXiv. Bharat Hunt files it under AI Models — the section covering a new or updated model, its capabilities, benchmarks or availability.
AssameseLM-E2B: An Open-Weight Assamese Language Model for Efficient Consumer-Grade Deployment. It centres on Gemma, and also names Fine-tuning and RAG. Reported by arXiv. Bharat Hunt files it under Open Source AI and AI Models — the section covering open-weight models, permissive licences and community releases.
Bharat Hunt links to original reporting and does not republish it. Headlines and links belong to their publishers; summaries and the trend ranking are ours. All AI stories
Stories in the last 24 hours, against the 24 before it. A change is shown only where the earlier window had enough coverage to compare against.
Our own 0-100 ranking, not an industry metric and not anyone else’s “trending” number. It combines how recently a story was covered (32%), how many independent publications covered it (28%), how fast that coverage is arriving right now (20%), how reliable those sources are (12%) and how many people opened the story (8%).
A story we cannot date carries no score at all rather than a made-up one, so some cards show no badge. Summaries are written by Bharat Hunt from the headline and the coverage — the original article is always the source of truth, and every link goes to the publisher.